How would you approach scaling a database to handle increased traffic while ensuring data consistency?

During my final year, I worked on a group project to develop a Student Management System. As part of the team, I was responsible for the database design. Initially, we anticipated low traffic, but our application quickly gained interest during our presentation phase. To address this, I proposed that we implement database sharding. We divided the student data into different shards based on their year of study. This not only improved our performance but also ensured that data remained consistent across different shards by using a centralized mechanism for updates. We also used caching to store frequently accessed data, which reduced database load during peak times. This experience taught me the importance of planning for scalability from the start.

In my part-time job at a local retail store, I noticed that our inventory management system struggled during busy seasons, leading to errors in stock levels. To tackle this, I suggested that we implement a more robust inventory database. I collaborated with my supervisor to introduce a real-time updating system that tracked sales and inventory levels instantly. We used a simple database management tool that allowed us to scale our operations without compromising consistency. This experience helped me understand the balance between having a system that could handle traffic spikes while ensuring accurate data across all platforms, enhancing our ability to serve customers effectively.

In my first job at a startup, we faced rapid growth in user registrations, which put a strain on our database. I was part of a small team tasked with improving our database performance. We decided to implement horizontal scaling by adding more database servers. To ensure data consistency, we used a master-slave replication strategy where one server was the primary source of truth, while others handled read requests. This approach allowed us to manage increased traffic while minimizing the risk of data inconsistency. It was a hands-on experience that taught me the importance of balancing performance and data integrity in real-world applications.